Artificial intelligence is SAS: Six Questions chinese singapore phone number list expanding in many fields? but its growing adoption has highlighted a key challenge: access to quality data . The issue is not so much the quantity of data available? but its quality and compliance with regulations. Many existing datasets are lacking? lack representativeness? or do not meet legal requirements. In this scenario? synthetic data emerges as a viable alternative to address these challenges.
Synthetic data generation is transforming the way we manage and analyze data? enabling organizations to overcome the limitations of real data and opening up new possibilities for implementing innovative and scalable solutions to solve complex problems. The benefits are many? including the ability to develop and validate models ? protect data confidentiality ? and compensate for the lack of real data in specific contexts? creating simulated scenarios of financial transactions? health records? or consumer behavior patterns.
Six Essential Questions Before Using Synthetic Data
According to SAS ? to fully exploit the potential customers indicated difficulty understanding benefits of synthetic data? it is however essential to ask the right questions? in order to guarantee its effectiveness and reliability. “By asking ourselves six essential questions before generating synthetic data? we can ensure that the data created is of high quality? preserves privacy and effectively serves its intended purpose ?” comments Nicola Scarfone ? Generative AI Team Leader at SAS
What is the purpose of generating synthetic data? Understanding why you want to generate synthetic data is essential to setting up the process effectively. For example? if you are looking to augment an existing dataset? simulate rare scenarios? or protect privacy? but have limited real-world data? synthetic data can be useful because it can train machine learning models. Having a clear goal helps you choose the right tools and ensure that the data you generate is truly useful for the context in which it will be applied.
What methods should be used to generate synthetic data?
There are several strategies to generate synthetic data? each with its advantages and limitations. A simple approach is to apply b2c fax predefined rules? based on known patterns? statistical distributions or sets of plausible values. However? this method may not be effective when the relationships between the data are complex. For more advanced scenarios? algorithmic or artificial intelligence-based techniques can be used. Generative Adversarial Networks (GANs) are particularly effective in creating realistic data through a system of competition between neural networks. The SMOTE (Synthetic Minority Over-sampling Technique) method is useful for rebalancing unbalanced datasets? while agent-based modeling allows simulating complex dynamics. The choice of method will therefore depend on the specific needs of the project.
How to ensure the quality and validity of synthetic data? For synthetic data to be truly useful? it must faithfully reflect the statistical characteristics and correlations present in real data. This means analyzing and comparing the generated data with the original data? checking the consistency of distributions and relationships between variables. Using statistical metrics and visualization tools helps assess the quality of synthetic data. If the synthetic data is unrealistic or inconsistent? it can compromise the performance of machine learning models and lead to incorrect decisions.
How to address privacy and security concerns?
One of the main advantages of synthetic data is the possibility of preserving user privacy? but it is important to ensure that it does not contain information that can be traced back to the original data. To reduce the risk of re-identification? techniques such as differential privacy can be adopted? which introduces controlled variations in the data to make it impossible to connect it to real individuals. Furthermore? it is essential to apply adequate security measures to protect synthetic data from unauthorized access? thus ensuring safe use and compliance with privacy regulations.
What are the potential biases in synthetic data? Synthetic data can contain biases? just like real data? and if not identified and corrected? they can negatively impact machine learning models and analyses. It is therefore important to identify any imbalances in the original data and adopt strategies to avoid amplifying them in the generated data. Careful analysis of data distributions and segments helps to detect and correct any biases? helping to create fairer and more reliable models.